library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
The data to process is described in:
https://zenodo.org/record/4156647#.Y1bSF3bMKUk
IR Saliva Testing Dataset
10.5281/zenodo.4156647 https://doi.org/10.5281/zenodo.4156647
I added a column to the data identifying the repeated experiments.
SalivaIR <- as.data.frame(read_excel("~/GitHub/FCA/Data/SalivaThermal_Source_Data_2.xlsx"))
SalivaIR_set1 <- subset(SalivaIR,RepID==1)
rownames(SalivaIR_set1) <- SalivaIR_set1$ID
SalivaIR_set1$RepID <- NULL
SalivaIR_set1$ID <- NULL
SalivaIR_set1$Ct <- NULL
SalivaIR_set2 <- subset(SalivaIR,RepID==2)
rownames(SalivaIR_set2) <- SalivaIR_set2$ID
SalivaIR_set2$RepID <- NULL
SalivaIR_set2$ID <- NULL
SalivaIR_set2$Ct <- NULL
SalivaIR_set3 <- subset(SalivaIR,RepID==3)
rownames(SalivaIR_set3) <- SalivaIR_set3$ID
SalivaIR_set3$RepID <- NULL
SalivaIR_set3$ID <- NULL
SalivaIR_set3$Ct <- NULL
SalivaIR_Avg <- (SalivaIR_set1 + SalivaIR_set2 + SalivaIR_set3)/3
colnames(SalivaIR_Avg) <- paste("V",colnames(SalivaIR_Avg),sep="_")
SalivaIR_Avg$class <- 1*(str_detect(rownames(SalivaIR_Avg),"P"))
pander::pander(table(SalivaIR_Avg$class))
| 0 | 1 |
|---|---|
| 30 | 31 |
studyName <- "IRSaliva"
dataframe <- SalivaIR_Avg
outcome <- "class"
TopVariables <- 10
thro <- 0.80
cexheat = 0.15
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 61 | 251 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 30 | 31 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) >= 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.999994
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> Included: 224 , Uni p: 0.04988877 , Uncorrelated Base: 1 , Outcome-Driven Size: 0 , Base Size: 1
#>
#>
1 <R=1.000,r=0.975,N= 224>, Top: 2( 53 )[ 1 : 2 Fa= 2 : 0.975 ]( 2 , 147 , 0 ),<|>Tot Used: 149 , Added: 147 , Zero Std: 0 , Max Cor: 1.000
#>
2 <R=1.000,r=0.975,N= 224>, Top: 9( 74 )[ 1 : 9 Fa= 11 : 0.975 ]( 9 , 121 , 2 ),<|>Tot Used: 224 , Added: 121 , Zero Std: 0 , Max Cor: 1.000
#>
3 <R=1.000,r=0.975,N= 224>, Top: 27( 9 )[ 1 : 27 Fa= 38 : 0.975 ]( 27 , 100 , 11 ),<|>Tot Used: 224 , Added: 100 , Zero Std: 0 , Max Cor: 1.000
#>
4 <R=1.000,r=0.975,N= 224>, Top: 46( 5 )=[ 2 : 46 Fa= 83 : 0.989 ]( 45 , 90 , 38 ),<|>Tot Used: 224 , Added: 90 , Zero Std: 0 , Max Cor: 1.000
#>
5 <R=1.000,r=0.975,N= 224>, Top: 23( 2 )[ 1 : 23 Fa= 106 : 0.975 ]( 23 , 36 , 83 ),<|>Tot Used: 224 , Added: 36 , Zero Std: 0 , Max Cor: 0.999
#>
6 <R=0.999,r=0.974,N= 224>, Top: 11( 1 )[ 1 : 11 Fa= 116 : 0.974 ]( 11 , 12 , 106 ),<|>Tot Used: 224 , Added: 12 , Zero Std: 0 , Max Cor: 0.996
#>
7 <R=0.996,r=0.948,N= 118>, Top: 51( 1 )[ 1 : 51 Fa= 125 : 0.948 ]( 50 , 57 , 116 ),<|>Tot Used: 224 , Added: 57 , Zero Std: 0 , Max Cor: 0.998
#>
8 <R=0.998,r=0.949,N= 118>, Top: 30( 1 )[ 1 : 30 Fa= 132 : 0.949 ]( 30 , 33 , 125 ),<|>Tot Used: 224 , Added: 33 , Zero Std: 0 , Max Cor: 0.992
#>
9 <R=0.992,r=0.946,N= 118>, Top: 19( 1 )[ 1 : 19 Fa= 134 : 0.946 ]( 18 , 21 , 132 ),<|>Tot Used: 224 , Added: 21 , Zero Std: 0 , Max Cor: 0.991
#>
10 <R=0.991,r=0.945,N= 118>, Top: 3( 1 )[ 1 : 3 Fa= 134 : 0.945 ]( 3 , 3 , 134 ),<|>Tot Used: 224 , Added: 3 , Zero Std: 0 , Max Cor: 0.966
#>
11 <R=0.966,r=0.883,N= 106>, Top: 42( 1 )[ 1 : 42 Fa= 141 : 0.883 ]( 41 , 56 , 134 ),<|>Tot Used: 224 , Added: 56 , Zero Std: 0 , Max Cor: 0.972
#>
12 <R=0.972,r=0.886,N= 106>, Top: 13( 1 )[ 1 : 13 Fa= 143 : 0.886 ]( 12 , 13 , 141 ),<|>Tot Used: 224 , Added: 13 , Zero Std: 0 , Max Cor: 0.925
#>
13 <R=0.925,r=0.863,N= 106>, Top: 24( 1 )[ 1 : 24 Fa= 145 : 0.863 ]( 23 , 26 , 143 ),<|>Tot Used: 224 , Added: 26 , Zero Std: 0 , Max Cor: 0.992
#>
14 <R=0.992,r=0.896,N= 106>, Top: 4( 1 )[ 1 : 4 Fa= 145 : 0.896 ]( 4 , 4 , 145 ),<|>Tot Used: 224 , Added: 4 , Zero Std: 0 , Max Cor: 0.976
#>
15 <R=0.976,r=0.838,N= 44>, Top: 21( 1 )[ 1 : 21 Fa= 145 : 0.838 ]( 21 , 23 , 145 ),<|>Tot Used: 224 , Added: 23 , Zero Std: 0 , Max Cor: 0.974
#>
16 <R=0.974,r=0.837,N= 44>, Top: 7( 1 )[ 1 : 7 Fa= 146 : 0.837 ]( 7 , 7 , 145 ),<|>Tot Used: 224 , Added: 7 , Zero Std: 0 , Max Cor: 0.894
#>
17 <R=0.894,r=0.800,N= 44>, Top: 29( 2 )[ 1 : 29 Fa= 147 : 0.800 ]( 26 , 32 , 146 ),<|>Tot Used: 224 , Added: 32 , Zero Std: 0 , Max Cor: 0.973
#>
18 <R=0.973,r=0.837,N= 44>, Top: 5( 1 )[ 1 : 5 Fa= 149 : 0.837 ]( 5 , 6 , 147 ),<|>Tot Used: 224 , Added: 6 , Zero Std: 0 , Max Cor: 0.955
#>
19 <R=0.955,r=0.827,N= 44>, Top: 1( 1 )[ 1 : 1 Fa= 149 : 0.827 ]( 1 , 1 , 149 ),<|>Tot Used: 224 , Added: 1 , Zero Std: 0 , Max Cor: 0.825
#>
20 <R=0.825,r=0.800,N= 7>, Top: 3( 1 )[ 1 : 3 Fa= 150 : 0.800 ]( 3 , 4 , 149 ),<|>Tot Used: 224 , Added: 4 , Zero Std: 0 , Max Cor: 0.881
#>
21 <R=0.881,r=0.800,N= 7>, Top: 2( 1 )[ 1 : 2 Fa= 150 : 0.800 ]( 2 , 2 , 150 ),<|>Tot Used: 224 , Added: 2 , Zero Std: 0 , Max Cor: 0.795
#>
22 <R=0.795,r=0.800,N= 0>
#>
[ 22 ], 0.7947222 Decor Dimension: 224 Nused: 224 . Cor to Base: 56 , ABase: 1 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
5.5
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
0.0295
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
5.08
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
0.643
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPSTM <- attr(DEdataframe,"UPSTM")
gplots::heatmap.2(1.0*(abs(UPSTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after IDeA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.7947222
if (nrow(dataframe) < 1000)
{
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}
if (nrow(dataframe) < 1000)
{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
100 : V_1064 200 : V_854
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
100 : La_V_1064 200 : La_V_854
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##top variables
topvar <- c(1:length(varlist)) <= TopVariables
tableRaw <- univarRAW$orderframe[topvar,univariate_columns]
pander::pander(tableRaw)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| V_908 | 0.221 | 0.128 | 0.261 | 0.117 | 0.579 | 0.596 |
| V_906 | 0.220 | 0.127 | 0.261 | 0.117 | 0.585 | 0.596 |
| V_904 | 0.220 | 0.127 | 0.261 | 0.117 | 0.592 | 0.596 |
| V_892 | 0.219 | 0.127 | 0.261 | 0.121 | 0.626 | 0.596 |
| V_890 | 0.219 | 0.127 | 0.261 | 0.121 | 0.616 | 0.596 |
| V_888 | 0.219 | 0.127 | 0.261 | 0.122 | 0.603 | 0.596 |
| V_912 | 0.223 | 0.129 | 0.263 | 0.117 | 0.604 | 0.595 |
| V_910 | 0.222 | 0.128 | 0.262 | 0.117 | 0.587 | 0.595 |
| V_896 | 0.220 | 0.127 | 0.261 | 0.120 | 0.620 | 0.595 |
| V_894 | 0.219 | 0.127 | 0.261 | 0.121 | 0.625 | 0.595 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
pander::pander(finalTable)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| La_V_984 | -1.77e-04 | 5.79e-04 | 5.00e-04 | 6.60e-04 | 0.0433 | 0.810 |
| La_V_926 | 3.01e-06 | 1.00e-05 | 1.32e-05 | 1.06e-05 | 0.8339 | 0.791 |
| La_V_1204 | -1.17e-06 | 1.26e-05 | -1.46e-05 | 1.21e-05 | 0.8554 | 0.789 |
| La_V_888 | -4.82e-05 | 4.51e-04 | 2.98e-04 | 2.26e-04 | 0.5083 | 0.782 |
| La_V_1110 | 2.65e-05 | 8.94e-05 | -5.42e-05 | 9.75e-05 | 0.0657 | 0.778 |
| La_V_924 | -1.77e-05 | 6.72e-04 | -4.71e-04 | 6.34e-04 | 0.1659 | 0.778 |
| La_V_1214 | 5.07e-04 | 1.13e-03 | 1.42e-03 | 8.02e-04 | 0.7269 | 0.762 |
| La_V_964 | 1.27e-03 | 2.00e-03 | -5.81e-04 | 2.04e-03 | 0.3591 | 0.759 |
| La_V_1172 | 3.52e-06 | 3.58e-04 | 1.13e-04 | 1.78e-04 | 0.2284 | 0.742 |
| La_V_1096 | -2.02e-03 | 1.59e-02 | -9.15e-03 | 1.07e-02 | 0.1704 | 0.733 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 4.83 | 223 | 0.996 |
theCharformulas <- attr(dc,"LatentCharFormulas")
finalTable <- rbind(finalTable,tableRaw[topvar[!(topvar %in% topLAvar)],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- theCharformulas[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| La_V_984 | + (0.025)V_1138 - (0.040)V_1032 + V_984 - (0.987)V_982 | -1.77e-04 | 5.79e-04 | 5.00e-04 | 6.60e-04 | 0.0433 | 0.810 | 0.582 | -3 |
| La_V_926 | + (0.138)V_930 - (0.604)V_928 + V_926 - (0.752)V_924 + (0.217)V_922 - (3.18e-05)V_878 | 3.01e-06 | 1.00e-05 | 1.32e-05 | 1.06e-05 | 0.8339 | 0.791 | 0.589 | -5 |
| La_V_1204 | - (4.11e-04)V_1300 - (0.262)V_1206 + V_1204 - (1.415)V_1202 + (0.882)V_1200 - (0.205)V_1198 | -1.17e-06 | 1.26e-05 | -1.46e-05 | 1.21e-05 | 0.8554 | 0.789 | 0.560 | -5 |
| La_V_888 | + V_888 - (1.657)V_884 + (0.655)V_878 | -4.82e-05 | 4.51e-04 | 2.98e-04 | 2.26e-04 | 0.5083 | 0.782 | 0.596 | 1 |
| La_V_1110 | - (5.55e-04)V_1138 + V_1110 - (1.754)V_1108 + (0.806)V_1104 + (1.084)V_1096 - (1.962)V_1094 + (0.826)V_1092 | 2.65e-05 | 8.94e-05 | -5.42e-05 | 9.75e-05 | 0.0657 | 0.778 | 0.561 | -5 |
| La_V_924 | + V_924 - (0.999)V_922 - (4.98e-03)V_878 | -1.77e-05 | 6.72e-04 | -4.71e-04 | 6.34e-04 | 0.1659 | 0.778 | 0.588 | 1 |
| La_V_1214 | + (0.040)V_1300 - (1.033)V_1216 + V_1214 | 5.07e-04 | 1.13e-03 | 1.42e-03 | 8.02e-04 | 0.7269 | 0.762 | 0.556 | 2 |
| La_V_964 | + (0.069)V_1138 - (1.035)V_972 + V_964 - (6.837)V_878 + (6.813)V_876 | 1.27e-03 | 2.00e-03 | -5.81e-04 | 2.04e-03 | 0.3591 | 0.759 | 0.585 | 0 |
| La_V_1172 | + (3.03e-04)V_1300 + (0.970)V_1176 - (1.971)V_1174 + V_1172 | 3.52e-06 | 3.58e-04 | 1.13e-04 | 1.78e-04 | 0.2284 | 0.742 | 0.559 | -1 |
| La_V_1096 | + (11.525)V_1138 - (12.543)V_1136 + V_1096 | -2.02e-03 | 1.59e-02 | -9.15e-03 | 1.07e-02 | 0.1704 | 0.733 | 0.559 | 13 |
| V_908 | NA | 2.21e-01 | 1.28e-01 | 2.61e-01 | 1.17e-01 | 0.5785 | 0.596 | 0.596 | NA |
| V_906 | NA | 2.20e-01 | 1.27e-01 | 2.61e-01 | 1.17e-01 | 0.5848 | 0.596 | 0.596 | NA |
| V_904 | NA | 2.20e-01 | 1.27e-01 | 2.61e-01 | 1.17e-01 | 0.5918 | 0.596 | 0.596 | NA |
| V_892 | NA | 2.19e-01 | 1.27e-01 | 2.61e-01 | 1.21e-01 | 0.6256 | 0.596 | 0.596 | NA |
| V_890 | NA | 2.19e-01 | 1.27e-01 | 2.61e-01 | 1.21e-01 | 0.6163 | 0.596 | 0.596 | NA |
| V_888 | NA | 2.19e-01 | 1.27e-01 | 2.61e-01 | 1.22e-01 | 0.6032 | 0.596 | 0.596 | NA |
| V_912 | NA | 2.23e-01 | 1.29e-01 | 2.63e-01 | 1.17e-01 | 0.6041 | 0.595 | 0.595 | NA |
| V_910 | NA | 2.22e-01 | 1.28e-01 | 2.62e-01 | 1.17e-01 | 0.5866 | 0.595 | 0.595 | NA |
| V_896 | NA | 2.20e-01 | 1.27e-01 | 2.61e-01 | 1.20e-01 | 0.6202 | 0.595 | 0.595 | NA |
| V_894 | NA | 2.19e-01 | 1.27e-01 | 2.61e-01 | 1.21e-01 | 0.6248 | 0.595 | 0.595 | NA |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,scale. = TRUE) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 30 | 0 |
| 1 | 17 | 14 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.721 | 0.592 | 0.829 |
| 3 | se | 0.452 | 0.273 | 0.640 |
| 4 | sp | 1.000 | 0.884 | 1.000 |
| 6 | diag.or | Inf | NA | Inf |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 29 | 1 |
| 1 | 5 | 26 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.902 | 0.798 | 0.963 |
| 3 | se | 0.839 | 0.663 | 0.945 |
| 4 | sp | 0.967 | 0.828 | 0.999 |
| 6 | diag.or | 150.800 | 16.521 | 1376.474 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 23 | 7 |
| 1 | 3 | 28 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.836 | 0.719 | 0.918 |
| 3 | se | 0.903 | 0.742 | 0.980 |
| 4 | sp | 0.767 | 0.577 | 0.901 |
| 6 | diag.or | 30.667 | 7.117 | 132.134 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 29 | 1 |
| 1 | 16 | 15 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.721 | 0.592 | 0.829 |
| 3 | se | 0.484 | 0.302 | 0.669 |
| 4 | sp | 0.967 | 0.828 | 0.999 |
| 6 | diag.or | 27.188 | 3.282 | 225.207 |
par(op)